"""
BSD 3-Clause License

Copyright (c) Soumith Chintala 2016,
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
  and/or other materials provided with the distribution.

* Neither the name of the copyright holder nor the names of its
  contributors may be used to endorse or promote products derived from
  this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.



Copyright 2020 Huawei Technologies Co., Ltd

Licensed under the BSD 3-Clause License (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

https://spdx.org/licenses/BSD-3-Clause.html

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
Code source: https://github.com/pytorch/vision
"""
from __future__ import division, absolute_import
import torch
import torch.utils.model_zoo as model_zoo
from torch import nn

__all__ = [
    'shufflenet_v2_x0_5', 'shufflenet_v2_x1_0', 'shufflenet_v2_x1_5',
    'shufflenet_v2_x2_0'
]

model_urls = {
    'shufflenetv2_x0.5':
    'https://download.pytorch.org/models/shufflenetv2_x0.5-f707e7126e.pth',
    'shufflenetv2_x1.0':
    'https://download.pytorch.org/models/shufflenetv2_x1-5666bf0f80.pth',
    'shufflenetv2_x1.5': None,
    'shufflenetv2_x2.0': None,
}


def channel_shuffle(x, groups):
    batchsize, num_channels, height, width = x.data.size()
    channels_per_group = num_channels // groups

    # reshape
    x = x.view(batchsize, groups, channels_per_group, height, width)

    x = torch.transpose(x, 1, 2).contiguous()

    # flatten
    x = x.view(batchsize, -1, height, width)

    return x


class InvertedResidual(nn.Module):

    def __init__(self, inp, oup, stride):
        super(InvertedResidual, self).__init__()

        if not (1 <= stride <= 3):
            raise ValueError('illegal stride value')
        self.stride = stride

        branch_features = oup // 2
        assert (self.stride != 1) or (inp == branch_features << 1)

        if self.stride > 1:
            self.branch1 = nn.Sequential(
                self.depthwise_conv(
                    inp, inp, kernel_size=3, stride=self.stride, padding=1
                ),
                nn.BatchNorm2d(inp),
                nn.Conv2d(
                    inp,
                    branch_features,
                    kernel_size=1,
                    stride=1,
                    padding=0,
                    bias=False
                ),
                nn.BatchNorm2d(branch_features),
                nn.ReLU(inplace=True),
            )

        self.branch2 = nn.Sequential(
            nn.Conv2d(
                inp if (self.stride > 1) else branch_features,
                branch_features,
                kernel_size=1,
                stride=1,
                padding=0,
                bias=False
            ),
            nn.BatchNorm2d(branch_features),
            nn.ReLU(inplace=True),
            self.depthwise_conv(
                branch_features,
                branch_features,
                kernel_size=3,
                stride=self.stride,
                padding=1
            ),
            nn.BatchNorm2d(branch_features),
            nn.Conv2d(
                branch_features,
                branch_features,
                kernel_size=1,
                stride=1,
                padding=0,
                bias=False
            ),
            nn.BatchNorm2d(branch_features),
            nn.ReLU(inplace=True),
        )

    @staticmethod
    def depthwise_conv(i, o, kernel_size, stride=1, padding=0, bias=False):
        return nn.Conv2d(
            i, o, kernel_size, stride, padding, bias=bias, groups=i
        )

    def forward(self, x):
        if self.stride == 1:
            x1, x2 = x.chunk(2, dim=1)
            out = torch.cat((x1, self.branch2(x2)), dim=1)
        else:
            out = torch.cat((self.branch1(x), self.branch2(x)), dim=1)

        out = channel_shuffle(out, 2)

        return out


class ShuffleNetV2(nn.Module):
    """ShuffleNetV2.
    
    Reference:
        Ma et al. ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design. ECCV 2018.

    Public keys:
        - ``shufflenet_v2_x0_5``: ShuffleNetV2 x0.5.
        - ``shufflenet_v2_x1_0``: ShuffleNetV2 x1.0.
        - ``shufflenet_v2_x1_5``: ShuffleNetV2 x1.5.
        - ``shufflenet_v2_x2_0``: ShuffleNetV2 x2.0.
    """

    def __init__(
        self, num_classes, loss, stages_repeats, stages_out_channels, **kwargs
    ):
        super(ShuffleNetV2, self).__init__()
        self.loss = loss

        if len(stages_repeats) != 3:
            raise ValueError(
                'expected stages_repeats as list of 3 positive ints'
            )
        if len(stages_out_channels) != 5:
            raise ValueError(
                'expected stages_out_channels as list of 5 positive ints'
            )
        self._stage_out_channels = stages_out_channels

        input_channels = 3
        output_channels = self._stage_out_channels[0]
        self.conv1 = nn.Sequential(
            nn.Conv2d(input_channels, output_channels, 3, 2, 1, bias=False),
            nn.BatchNorm2d(output_channels),
            nn.ReLU(inplace=True),
        )
        input_channels = output_channels

        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        stage_names = ['stage{}'.format(i) for i in [2, 3, 4]]
        for name, repeats, output_channels in zip(
            stage_names, stages_repeats, self._stage_out_channels[1:]
        ):
            seq = [InvertedResidual(input_channels, output_channels, 2)]
            for i in range(repeats - 1):
                seq.append(
                    InvertedResidual(output_channels, output_channels, 1)
                )
            setattr(self, name, nn.Sequential(*seq))
            input_channels = output_channels

        output_channels = self._stage_out_channels[-1]
        self.conv5 = nn.Sequential(
            nn.Conv2d(input_channels, output_channels, 1, 1, 0, bias=False),
            nn.BatchNorm2d(output_channels),
            nn.ReLU(inplace=True),
        )
        self.global_avgpool = nn.AdaptiveAvgPool2d((1, 1))

        self.classifier = nn.Linear(output_channels, num_classes)

    def featuremaps(self, x):
        x = self.conv1(x)
        x = self.maxpool(x)
        x = self.stage2(x)
        x = self.stage3(x)
        x = self.stage4(x)
        x = self.conv5(x)
        return x

    def forward(self, x):
        f = self.featuremaps(x)
        v = self.global_avgpool(f)
        v = v.view(v.size(0), -1)

        if not self.training:
            return v

        y = self.classifier(v)

        if self.loss == 'softmax':
            return y
        elif self.loss == 'triplet':
            return y, v
        else:
            raise KeyError("Unsupported loss: {}".format(self.loss))


def init_pretrained_weights(model, model_url):
    """Initializes model with pretrained weights.
    
    Layers that don't match with pretrained layers in name or size are kept unchanged.
    """
    if model_url is None:
        import warnings
        warnings.warn(
            'ImageNet pretrained weights are unavailable for this model'
        )
        return
    pretrain_dict = model_zoo.load_url(model_url)
    model_dict = model.state_dict()
    pretrain_dict = {
        k: v
        for k, v in pretrain_dict.items()
        if k in model_dict and model_dict[k].size() == v.size()
    }
    model_dict.update(pretrain_dict)
    model.load_state_dict(model_dict)


def shufflenet_v2_x0_5(num_classes, loss='softmax', pretrained=True, **kwargs):
    model = ShuffleNetV2(
        num_classes, loss, [4, 8, 4], [24, 48, 96, 192, 1024], **kwargs
    )
    if pretrained:
        init_pretrained_weights(model, model_urls['shufflenetv2_x0.5'])
    return model


def shufflenet_v2_x1_0(num_classes, loss='softmax', pretrained=True, **kwargs):
    model = ShuffleNetV2(
        num_classes, loss, [4, 8, 4], [24, 116, 232, 464, 1024], **kwargs
    )
    if pretrained:
        init_pretrained_weights(model, model_urls['shufflenetv2_x1.0'])
    return model


def shufflenet_v2_x1_5(num_classes, loss='softmax', pretrained=True, **kwargs):
    model = ShuffleNetV2(
        num_classes, loss, [4, 8, 4], [24, 176, 352, 704, 1024], **kwargs
    )
    if pretrained:
        init_pretrained_weights(model, model_urls['shufflenetv2_x1.5'])
    return model


def shufflenet_v2_x2_0(num_classes, loss='softmax', pretrained=True, **kwargs):
    model = ShuffleNetV2(
        num_classes, loss, [4, 8, 4], [24, 244, 488, 976, 2048], **kwargs
    )
    if pretrained:
        init_pretrained_weights(model, model_urls['shufflenetv2_x2.0'])
    return model
